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Censored Observations (censored + observation)
Selected AbstractsSemiparametric variance-component models for linkage and association analyses of censored trait dataGENETIC EPIDEMIOLOGY, Issue 7 2006G. Diao Abstract Variance-component (VC) models are widely used for linkage and association mapping of quantitative trait loci in general human pedigrees. Traditional VC methods assume that the trait values within a family follow a multivariate normal distribution and are fully observed. These assumptions are violated if the trait data contain censored observations. When the trait pertains to age at onset of disease, censoring is inevitable because of loss to follow-up and limited study duration. Censoring also arises when the trait assay cannot detect values below (or above) certain thresholds. The latent trait values tend to have a complex distribution. Applying traditional VC methods to censored trait data would inflate type I error and reduce power. We present valid and powerful methods for the linkage and association analyses of censored trait data. Our methods are based on a novel class of semiparametric VC models, which allows an arbitrary distribution for the latent trait values. We construct appropriate likelihood for the observed data, which may contain left or right censored observations. The maximum likelihood estimators are approximately unbiased, normally distributed, and statistically efficient. We develop stable and efficient numerical algorithms to implement the corresponding inference procedures. Extensive simulation studies demonstrate that the proposed methods outperform the existing ones in practical situations. We provide an application to the age at onset of alcohol dependence data from the Collaborative Study on the Genetics of Alcoholism. A computer program is freely available. Genet. Epidemiol. 2006. © 2006 Wiley-Liss, Inc. [source] Long-memory dynamic Tobit modelsJOURNAL OF FORECASTING, Issue 5 2006A. E. Brockwell Abstract We introduce a long-memory dynamic Tobit model, defining it as a censored version of a fractionally integrated Gaussian ARMA model, which may include seasonal components and/or additional regression variables. Parameter estimation for such a model using standard techniques is typically infeasible, since the model is not Markovian, cannot be expressed in a finite-dimensional state-space form, and includes censored observations. Furthermore, the long-memory property renders a standard Gibbs sampling scheme impractical. Therefore we introduce a new Markov chain Monte Carlo sampling scheme, which is orders of magnitude more efficient than the standard Gibbs sampler. The method is inherently capable of handling missing observations. In case studies, the model is fit to two time series: one consisting of volumes of requests to a hard disk over time, and the other consisting of hourly rainfall measurements in Edinburgh over a 2-year period. The resulting posterior distributions for the fractional differencing parameter demonstrate, for these two time series, the importance of the long-memory structure in the models.,,Copyright © 2006 John Wiley & Sons, Ltd. [source] Non-parametric tests for distributional treatment effect for randomly censored responsesJOURNAL OF THE ROYAL STATISTICAL SOCIETY: SERIES B (STATISTICAL METHODOLOGY), Issue 1 2009Myoung-jae Lee Summary., For a binary treatment ,=0, 1 and the corresponding ,potential response'Y0 for the control group (,=0) and Y1 for the treatment group (,=1), one definition of no treatment effect is that Y0 and Y1 follow the same distribution given a covariate vector X. Koul and Schick have provided a non-parametric test for no distributional effect when the realized response (1,,)Y0+,Y1 is fully observed and the distribution of X is the same across the two groups. This test is thus not applicable to censored responses, nor to non-experimental (i.e. observational) studies that entail different distributions of X across the two groups. We propose ,X -matched' non-parametric tests generalizing the test of Koul and Schick following an idea of Gehan. Our tests are applicable to non-experimental data with randomly censored responses. In addition to these motivations, the tests have several advantages. First, they have the intuitive appeal of comparing all available pairs across the treatment and control groups, instead of selecting a number of matched controls (or treated) in the usual pair or multiple matching. Second, whereas most matching estimators or tests have a non-overlapping support (of X) problem across the two groups, our tests have a built-in protection against the problem. Third, Gehan's idea allows the tests to make good use of censored observations. A simulation study is conducted, and an empirical illustration for a job training effect on the duration of unemployment is provided. [source] Monitoring processes with data censored owing to competing risks by using exponentially weighted moving average control chartsJOURNAL OF THE ROYAL STATISTICAL SOCIETY: SERIES C (APPLIED STATISTICS), Issue 3 2001Stefan H. Steiner In industry, process monitoring is widely employed to detect process changes rapidly. However, in some industrial applications observations are censored. For example, when testing breaking strengths and failure times often a limited stress test is performed. With censored observations, a direct application of traditional monitoring procedures is not appropriate. When the censoring occurs due to competing risks, we propose a control chart based on conditional expected values to detect changes in the mean strength. To protect against possible confounding caused by changes in the mean of the censoring mechanism we also suggest a similar chart to detect changes in the mean censoring level. We provide an example of monitoring bond strength to illustrate the application of this methodology. [source] Efficacy of Enalapril for Prevention of Congestive Heart Failure in Dogs with Myxomatous Valve Disease and Asymptomatic Mitral RegurgitationJOURNAL OF VETERINARY INTERNAL MEDICINE, Issue 1 2002Clarence Kvart We evaluated the long-term effect of early angiotensin-converting enzyme (ACE) inhibition (enalapril maleate) as monotherapy to postpone or prevent congestive heart failure (CHF) in asymptomatic dogs with mitral regurgitation (MR) attributable to myxomatous valvular disease (MVD) in a prospective, randomized, double-blinded, placebo-controlled multicenter trial involving 14 centers in Scandinavia. Two hundred twenty-nine Cavalier King Charles (CKC) Spaniels with MR attributable to MVD but no signs of CHF were randomly allocated to treatment with enalapril 0.25,0.5 mg daily (n = 116) or to placebo groups (n = 113). Each dog was evaluated by physical examination, electrocardiography, and thoracic radiography at entry and every 12 months (±30 days). The number of dogs developing heart failure was similar in the treatment and placebo groups (n = 50 [43%] and n = 48 [42%], respectively; P= .99). The estimated means, adjusted for censored observations, for the period from initiation of therapy to heart failure were 1,150 ± 50 days for dogs in the treatment group and 1,130 ± 50 days for dogs in the placebo group (P= .85). When absence or presence of cardiomegaly at the entrance of the trial was considered, there were still no differences between the treatment and placebo groups (P= .98 and .51, respectively). Multivariate analysis showed that enalapril had no significant effect on the time from initiation of therapy to heart failure (P= .86). Long-term treatment with enalapril in asymptomatic dogs with MVD and MR did not delay the onset of heart failure regardless of whether or not cardiomegaly was present at initiation of the study. [source] Measuring Agreement of Multivariate Discrete Survival Times Using a Modified Weighted Kappa CoefficientBIOMETRICS, Issue 1 2009Ying Guo Summary Assessing agreement is often of interest in clinical studies to evaluate the similarity of measurements produced by different raters or methods on the same subjects. We present a modified weighted kappa coefficient to measure agreement between bivariate discrete survival times. The proposed kappa coefficient accommodates censoring by redistributing the mass of censored observations within the grid where the unobserved events may potentially happen. A generalized modified weighted kappa is proposed for multivariate discrete survival times. We estimate the modified kappa coefficients nonparametrically through a multivariate survival function estimator. The asymptotic properties of the kappa estimators are established and the performance of the estimators are examined through simulation studies of bivariate and trivariate survival times. We illustrate the application of the modified kappa coefficient in the presence of censored observations with data from a prostate cancer study. [source] Legendre polynomial kernel estimation of a density function with censored observations and an application to clinical trialsCOMMUNICATIONS ON PURE & APPLIED MATHEMATICS, Issue 8 2007Simeon M. Berman Let f(x), x , ,M, M , 1, be a density function on ,M, and X1, ,., Xn a sample of independent random vectors with this common density. For a rectangle B in ,M, suppose that the X's are censored outside B, that is, the value Xk is observed only if Xk , B. The restriction of f(x) to x , B is clearly estimable by established methods on the basis of the censored observations. The purpose of this paper is to show how to extrapolate a particular estimator, based on the censored sample, from the rectangle B to a specified rectangle C containing B. The results are stated explicitly for M = 1, 2, and are directly extendible to M , 3. For M = 2, the extrapolation from the rectangle B to the rectangle C is extended to the case where B and C are triangles. This is done by means of an elementary mapping of the positive quarter-plane onto the strip {(u, v): 0 , u , 1, v > 0}. This particular extrapolation is applied to the estimation of the survival distribution based on censored observations in clinical trials. It represents a generalization of a method proposed in 2001 by the author [2]. The extrapolator has the following form: For m , 1 and n , 1, let Km, n(x) be the classical kernel estimator of f(x), x , B, based on the orthonormal Legendre polynomial kernel of degree m and a sample of n observed vectors censored outside B. The main result, stated in the cases M = 1, 2, is an explicit bound for E|Km, n(x) , f(x)| for x , C, which represents the expected absolute error of extrapolation to C. It is shown that the extrapolator is a consistent estimator of f(x), x , C, if f is sufficiently smooth and if m and n both tend to , in a way that n increases sufficiently rapidly relative to m. © 2006 Wiley Periodicals, Inc. [source] | ||||||||||